Basic Concepts of Probability and Statistics in the Law

• Κύριος συγγραφέας: Finkelstein, Michael O. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: New York, NY : Springer New York : Imprint: Springer, 2009.
• Θέματα: Law.; Law > Philosophy.; Statistics.; Fundamentals of Law.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.

 This book sets out basic statistical tools as they have been applied in actual legal disputes. Examples range over diverse fields of law, such as identification evidence, mass torts, securities law, environmental regulation, and capital punishment, among many others. In some notable cases, such as the U.S. Supreme Court’s decision in Bush v. Gore, the book explores aspects of the statistical evidence that were unrecognized or misconceived by the parties or the court. For the statistics student, the book will give a deeper appreciation of foundation concepts and provide a wealth of real life applications. For the lawyer, or law student, the book will introduce a subject that has become increasingly important both in litigation and in studies of the legal system. The book is self-contained and may be read without background in probability or statistics. Michael O. Finkelstein is a lawyer and has been described as a pioneer in the use of statistics in law. He has for many years taught Statistics for Lawyers at Columbia Law School, and has also taught this subject at Harvard, Yale, and the University of Pennsylvania law schools. In addition to numerous articles, he is the author of Quantitative Methods in Law, published in 1978, and is the co-author with Professor Bruce Levin of Statistics for Lawyers, first published in 1990 and now in its second edition. 

 Probability -- Descriptive Tools -- Compound Events -- Significance -- Random Variables and Their Distributions -- Confidence -- Power -- Sampling -- Epidemiology -- Combining the Evidence -- Regression Models -- Regression Models – Further Considerations.